我是pytorch的新手,只是想写一个网络。data.shape
是(2046170),最后5列是一些标签。数据中的数字是浮点数,如0.030822
#%%
from sklearn.feature_selection import RFE
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split
import torch.functional as F
#%%
data = pd.read_table("table.log")
data_x = data.iloc[:, 0:(data.shape[1]-5)]
data_y = data.loc[:, 'target']
X_train, X_test, y_train, y_test = train_test_split(data_x,data_y,test_size=0.2,random_state=0)
#%%
from sklearn.linear_model import LinearRegression
lr = LinearRegression(normalize=True)
lr.fit(X_train,y_train)
rfe1 = RFE(estimator=lr,n_features_to_select=2000)
rfe1 = rfe1.fit(X_train,y_train)
#%%
x_train_rfe1 = X_train[X_train.columns[rfe1.support_]]
print(x_train_rfe1.head())
class testmodel(nn.Module):
def __init__(self):
super(testmodel,self).__init__()
self.conv = nn.Sequential(
nn.Conv1d(1500, 500, 1500, 0, 0),
nn.ReLU(),
nn.Conv1d(500, 100, 500, 0),
nn.ReLU(),
nn.Conv1d(100, 20, 100, 0),
nn.Sigmoid()
)
def forward(self,x):
x = self.conv
return x
#%%
x_train_rfe1 = torch.Tensor(x_train_rfe1.values)
y_train = torch.Tensor(y_train.values.astype(np.int64))
model = testmodel()
y = model(x_train_rfe1)
criterion = nn.MSELoss()
loss = criterion(y, y_train)
print(loss)
错误消息:
Traceback (most recent call last):
File "<input>", line 7, in <module>
File "/miniconda3/envs/ml/lib/python3.8/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/miniconda3/envs/ml/lib/python3.8/site-packages/torch/nn/modules/loss.py", line 431, in forward
return F.mse_loss(input, target, reduction=self.reduction)
File "/miniconda3/envs/ml/lib/python3.8/site-packages/torch/nn/functional.py", line 2203, in mse_loss
if not (target.size() == input.size()):
File "/miniconda3/envs/ml/lib/python3.8/site-packages/torch/nn/modules/module.py", line 575, in __getattr__
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'Sequential' object has no attribute 'size'
错误在哪里? 互联网通常是这样写的吗? 我怎样才能改进它
您从未在
forward
中的conv
序列层中运行输入张量x
做一些PyTorch教程,它们将帮助您了解基本知识:https://pytorch.org/tutorials/
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